Fusarium crown rot (FCR) is a chronic disease in many regions of the world in wheat, caused by Fusarium culmorum, Fusarium pseudograminearum, and Fusarium graminearum. The operational efficacy of pesticide application...Fusarium crown rot (FCR) is a chronic disease in many regions of the world in wheat, caused by Fusarium culmorum, Fusarium pseudograminearum, and Fusarium graminearum. The operational efficacy of pesticide applications using unmanned aerial vehicles (UAVs) significantly affects the biological efficacy of the pesticides. This study aimed to compare the effectiveness of unmanned aerial vehicle and field sprayer applications in controlling crown rot diseases frequently observed in wheat crops in the Thrace region, Turkey. A licensed fungicide containing the active ingredients, prochloraz plus trifloxystrobin plus cyproconazole mixture was applied to wheat during the ZGS 27 growth stage. The disease severity, disease incidence, and the effectiveness of fungicide treatment on disease severity (%) were evaluated for F. culmorum crown rot disease. The results showed that the severity of the disease during the seedling stage was 11.25% and 18.33% for unmanned aerial vehicle and field sprayer applications, respectively. In the harvest stage, the incidence of disease was 28.33%-39.99% and 48.75%-51.25%, respectively, and the effectiveness of unmanned aerial vehicle application was found to be high, approximately 52%, during the seedling and harvest stages. The unmanned aerial vehicle, acting similarly to the field sprayer, exhibited higher grain quality under conditions of stress from disease. Furthermore, spike weight, grain weight, and number of grains exhibited stronger positive correlations compared to unmanned aerial vehicle treatment. Therefore, unmanned aerial vehicles have promising potential as viable options to manage FCR when the prevailing environmental conditions are not conducive to the use of field sprayer. The results of this research will guide future studies to investigate the efficacy of UAVs on a wider range of pesticides and to further develop the technology to investigate its effectiveness, cost-effectiveness, and sustainability in agricultural applications.展开更多
The corrosion fatigue propagation behaviour of high strength low alloy Cr-Ni and Si-Mn steels has been investigated in 3.5 %NaCl solution spryly at the frequencies of 0.1 and 5.5 Hz. It was shown that the fatigue crac...The corrosion fatigue propagation behaviour of high strength low alloy Cr-Ni and Si-Mn steels has been investigated in 3.5 %NaCl solution spryly at the frequencies of 0.1 and 5.5 Hz. It was shown that the fatigue crack initiation resistances of the two steels are significantly re- duced in salt spray;the fatigue crack growth rates of steels increase with the decrease of fre- quency and are much higher in salt spray than in air within low ΔK range.A critical stress in- tensity factor was observed for each steel and the crack growth stoppage will occur if the ΔK values are lower than it.It was found that the active dissolution makes great contribution to the fatigue crack growth within low ΔK range.展开更多
Dynamic acquisition of crop morphology is beneficial to real-time variable decision of precise spraying operations in fields.However,the existing spraying quantity regulation has high tolerance on the statistical char...Dynamic acquisition of crop morphology is beneficial to real-time variable decision of precise spraying operations in fields.However,the existing spraying quantity regulation has high tolerance on the statistical characteristics of regional morphology,so expensive LiDAR and ultrasonic radar can’t make full use of their high accuracy,and can reduce decision speed because of too much detail of branches and leaves.Therefore,designing a novel recognition system embedded machine learning with low-cost monocular vision is more feasible,especially in China,where the agricultural implements are medium sizes and cost-sensitive.In addition,we found that the growth period of crops is an important reference index for guiding spraying.So,taking cotton as a case study,a cotton morphology acquisition by a single camera is established,and a cotton growth period recognition algorithm based on Convolution Neural Network(CNN)is proposed in this paper.Through the optimization process based on confusion matrix and recognition efficiency,an optimized CNN model structure is determined from 9 different model structures,and its reliability was verified by changing training sets and test sets many times based on the idea of kfold test.The accuracy,precision,recall,F1-score and recognition speed of this CNN model are 93.27%,95.39%,94.31%,94.76%and 71.46 ms per image,respectively.In addition,compared with the performance of VGG16 and AlexNet,the convolution neural network model proposed in this paper has better performance.Finally,in order to verify the reliability of the designed recognition system and the feasibility of the spray decision-making algorithm based on CNN,spraying deposition experiments were carried out with 3 different growthperiods of cotton.The experiments’results validate that after the optimal spray parameters were applied at different growth periods respectively,the average optimum index in 3 growth periods was 42.29%,which was increased up to 62.24%than the operations without distinguishing growth periods.展开更多
基金the student team members Arife Adak,Cagatay Dayan,Alara Uzuner,KemalÇelik,Aysenur Topcu,and Gurkan Simsek for their important contributions.
文摘Fusarium crown rot (FCR) is a chronic disease in many regions of the world in wheat, caused by Fusarium culmorum, Fusarium pseudograminearum, and Fusarium graminearum. The operational efficacy of pesticide applications using unmanned aerial vehicles (UAVs) significantly affects the biological efficacy of the pesticides. This study aimed to compare the effectiveness of unmanned aerial vehicle and field sprayer applications in controlling crown rot diseases frequently observed in wheat crops in the Thrace region, Turkey. A licensed fungicide containing the active ingredients, prochloraz plus trifloxystrobin plus cyproconazole mixture was applied to wheat during the ZGS 27 growth stage. The disease severity, disease incidence, and the effectiveness of fungicide treatment on disease severity (%) were evaluated for F. culmorum crown rot disease. The results showed that the severity of the disease during the seedling stage was 11.25% and 18.33% for unmanned aerial vehicle and field sprayer applications, respectively. In the harvest stage, the incidence of disease was 28.33%-39.99% and 48.75%-51.25%, respectively, and the effectiveness of unmanned aerial vehicle application was found to be high, approximately 52%, during the seedling and harvest stages. The unmanned aerial vehicle, acting similarly to the field sprayer, exhibited higher grain quality under conditions of stress from disease. Furthermore, spike weight, grain weight, and number of grains exhibited stronger positive correlations compared to unmanned aerial vehicle treatment. Therefore, unmanned aerial vehicles have promising potential as viable options to manage FCR when the prevailing environmental conditions are not conducive to the use of field sprayer. The results of this research will guide future studies to investigate the efficacy of UAVs on a wider range of pesticides and to further develop the technology to investigate its effectiveness, cost-effectiveness, and sustainability in agricultural applications.
文摘The corrosion fatigue propagation behaviour of high strength low alloy Cr-Ni and Si-Mn steels has been investigated in 3.5 %NaCl solution spryly at the frequencies of 0.1 and 5.5 Hz. It was shown that the fatigue crack initiation resistances of the two steels are significantly re- duced in salt spray;the fatigue crack growth rates of steels increase with the decrease of fre- quency and are much higher in salt spray than in air within low ΔK range.A critical stress in- tensity factor was observed for each steel and the crack growth stoppage will occur if the ΔK values are lower than it.It was found that the active dissolution makes great contribution to the fatigue crack growth within low ΔK range.
基金supported by National Natural Science Foundation of China(51475278)China Shandong Province Agricultural Machinery Equipment Research and Development Innovation Project(2018YF002)+2 种基金China Natural Science Foundation of Shandong Province(ZR2019PC024)China Scientific Research and Development Projects of Universities in Shandong Province(J18KA128)China and the Funds of Shandong‘Double Tops’Program(SYL2017XTTD14),China.
文摘Dynamic acquisition of crop morphology is beneficial to real-time variable decision of precise spraying operations in fields.However,the existing spraying quantity regulation has high tolerance on the statistical characteristics of regional morphology,so expensive LiDAR and ultrasonic radar can’t make full use of their high accuracy,and can reduce decision speed because of too much detail of branches and leaves.Therefore,designing a novel recognition system embedded machine learning with low-cost monocular vision is more feasible,especially in China,where the agricultural implements are medium sizes and cost-sensitive.In addition,we found that the growth period of crops is an important reference index for guiding spraying.So,taking cotton as a case study,a cotton morphology acquisition by a single camera is established,and a cotton growth period recognition algorithm based on Convolution Neural Network(CNN)is proposed in this paper.Through the optimization process based on confusion matrix and recognition efficiency,an optimized CNN model structure is determined from 9 different model structures,and its reliability was verified by changing training sets and test sets many times based on the idea of kfold test.The accuracy,precision,recall,F1-score and recognition speed of this CNN model are 93.27%,95.39%,94.31%,94.76%and 71.46 ms per image,respectively.In addition,compared with the performance of VGG16 and AlexNet,the convolution neural network model proposed in this paper has better performance.Finally,in order to verify the reliability of the designed recognition system and the feasibility of the spray decision-making algorithm based on CNN,spraying deposition experiments were carried out with 3 different growthperiods of cotton.The experiments’results validate that after the optimal spray parameters were applied at different growth periods respectively,the average optimum index in 3 growth periods was 42.29%,which was increased up to 62.24%than the operations without distinguishing growth periods.
